code: def create_fast_rcnn_predictor(conv_out, rois, fc_layers, cfg):
      # RCNN
      roi_out = roipooling(
          conv_out,
          rois,
          cntk.MAX_POOLING,
          (cfg["MODEL"].ROI_DIM, cfg["MODEL"].ROI_DIM),
          spatial_scale=1 / 16.0,
      )
      fc_out = fc_layers(roi_out)
  
      # prediction head
      W_pred = parameter(
          shape=(4096, cfg["DATA"].NUM_CLASSES),
          init=normal(scale=0.01),
          name="cls_score.W",
      )
      b_pred = parameter(
          shape=cfg["DATA"].NUM_CLASSES, init=0, name="cls_score.b"
      )
      cls_score = plus(times(fc_out, W_pred), b_pred, name="cls_score")
  
      # regression head
      W_regr = parameter(
          shape=(4096, cfg["DATA"].NUM_CLASSES * 4),
          init=normal(scale=0.001),
          name="bbox_regr.W",
      )
      b_regr = parameter(
          shape=cfg["DATA"].NUM_CLASSES * 4, init=0, name="bbox_regr.b"
      )
      bbox_pred = plus(times(fc_out, W_regr), b_regr, name="bbox_regr")
  
      return cls_score, bbox_pred
  
imports: from __future__ import print_function
  import numpy
  import os, sys
  import argparse
  import easydict
  import cntk
  from cntk import Trainer, load_model, Axis, input_variable, parameter, times, combine, softmax, roipooling, plus, element_times, CloneMethod, alias, Communicator, reduce_sum
  from cntk.core import Value
  from cntk.initializer import normal
  from cntk.layers import placeholder, Constant, Sequential
  from cntk.learners import momentum_sgd, learning_parameter_schedule_per_sample, momentum_schedule
  from cntk.logging import log_number_of_parameters, ProgressPrinter
  from cntk.logging.graph import find_by_name, plot
  from cntk.losses import cross_entropy_with_softmax
  from cntk.metrics import classification_error
  from _cntk_py import force_deterministic_algorithms
  from utils.rpn.cntk_smoothL1_loss import SmoothL1Loss
  from utils.annotations.annotations_helper import parse_class_map_file
  from utils.od_mb_source import ObjectDetectionMinibatchSource
  from utils.proposal_helpers import ProposalProvider
label_desc: Use image node_id 5 for calling a global function with example usage: create_fast_rcnn_predictor(conv_out, rois, fc_layers, cfg) and returns: cls_score,  bbox_pred